Topic overlap detection in messaging systems

ABSTRACT

Embodiments are provided for detecting overlapping topics in a messaging system. In an example system, a plurality of trigger phrases is received, where each trigger phrase is configured to trigger a bot that receives the trigger phrase to select a corresponding topic for conversation. For each trigger phrase, a vector representation is generated. Measures of similarity are generated based at least on the vector representations, where each measure of similarity represents a degree of similarity between a respective pair of vector representations. A topic overlap is detected based on a pair of vector representations having a measure of similarity above a similarity threshold, where the topic overlap indicates two trigger phrases that are overlapping. The topic overlap is provided to an authoring tool that comprises one or more interactive elements to enable a user to change at least one of the two trigger phrases that are overlapping.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Pat. Application No.17/744,241, filed May 13, 2022, entitled “Topic Overlap Detection inMessaging Systems,” which claims priority to U.S. Provisional Pat.Application No. 63/315,741, filed on Mar. 2, 2022, the entireties ofwhich are incorporated by reference herein.

BACKGROUND

Messaging systems, such as chat bots, may be utilized in a variety ofcontexts to simulate natural conversations with humans. In typicalsystems, a user query is parsed to identify a topic that best alignswith the query. Based on the identified topic, the chat bot provides aresponse to the user.

In existing systems, a user query may unintentionally align withmultiple topics. For example, a user may input a phrase to a chat bot,but due to the functioning of the chat bot, several possible topicsrelating to the phrase are identified. These overlapping topics resultin additional dialog between the user and the chat bot to determine anappropriate topic of interest.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

Methods, systems, apparatuses, and computer program products areprovided for detecting overlapping topics in a messaging system. In anexample system, a phrase representation generator is configured toreceive a plurality of trigger phrases, where each trigger phrase isconfigured to trigger a bot that receives the trigger phrase to select acorresponding topic for conversation. The phrase representationgenerator is also configured to generate, for each trigger phrase, avector representation. A topic overlap detector is configured togenerate measures of similarity based at least on the vectorrepresentations, where each measure of similarity represents a degree ofsimilarity between a respective pair of vector representations. Thetopic overlap detector detects a topic overlap based on a pair of vectorrepresentations having a measure of similarity above a similaritythreshold, where the topic overlap indicates two trigger phrases thatare overlapping. The topic overlap detector provides the topic overlapto an authoring tool that comprises one or more interactive elements toenable a user to change at least one of the two trigger phrases that areoverlapping. In this manner, authors of bots (e.g., chat bots) may seeand resolve overlapping topics, resulting in improved performance of themessaging system.

Further features and advantages of the disclosed subject matter, as wellas the structure and operation of various example embodiments, aredescribed in detail below with reference to the accompanying drawings.It is noted that the disclosed subject matter is not limited to thespecific embodiments described herein. Such example embodiments arepresented herein for illustrative purposes only. Additional exampleembodiments will be apparent to persons skilled in the relevant art(s)based on the teachings contained herein.

BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES

The accompanying drawings, which are incorporated herein and form a partof the specification, illustrate example embodiments of the presentapplication and, together with the description, further serve to explainthe principles of the example embodiments and to enable a person skilledin the pertinent art to make and use the example embodiments.

FIG. 1 shows a block diagram of a messaging system, according to anexample embodiment.

FIG. 2 shows a block diagram of a system for detecting overlappingtopics in a messaging system, according to an example embodiment.

FIG. 3 shows a flowchart of a method for detecting overlapping topics ina messaging system, in accordance with an example embodiment.

FIG. 4 shows a flowchart of a method for receiving a change to anoverlapping trigger phrase, in accordance with an example embodiment.

FIG. 5 shows a flowchart of a method for ranking a plurality of topicoverlaps, in accordance with an example embodiment.

FIG. 6 shows an illustrative user interface for presenting overlappingtopic information, according to an example embodiment.

FIG. 7 shows an illustrative user interface for presenting overlappingtopic information, according to an example embodiment.

FIG. 8 shows a block diagram of an example computing device that may beused to implement example embodiments.

The features and advantages of the disclosed subject matter will becomemore apparent from the detailed description set forth below when takenin conjunction with the drawings, in which like reference charactersidentify corresponding elements throughout. In the drawings, likereference numbers generally indicate identical, functionally similar,and/or structurally similar elements. The drawing in which an elementfirst appears is indicated by the leftmost digit(s) in the correspondingreference number.

DETAILED DESCRIPTION I. Introduction

The present specification and accompanying drawings disclose one or moreexample embodiments that incorporate the features of the disclosedsubject matter. The scope of the disclosed subject matter is not limitedto the example embodiments described herein. The example embodimentsmerely exemplify the disclosed subject matter, and modified versions ofthe disclosed embodiments are also encompassed. Example embodiments aredefined by the claims appended hereto.

References in the specification to “one embodiment,” “an embodiment,”“an example embodiment,” etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with an exampleembodiment, it is submitted that it is within the knowledge of oneskilled in the art to effect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

In the discussion, unless otherwise stated, adjectives such as“substantially” and “about” modifying a condition or relationshipcharacteristic of a feature or features of an example embodiment of thedisclosure, should be understood to mean that the condition orcharacteristic is defined to within tolerances that are acceptable foroperation of the example embodiment for an application for which it isintended.

If the performance of an operation is described herein as being “basedon” one or more factors, it is to be understood that the performance ofthe operation may be based solely on such factor(s) or may be based onsuch factor(s) along with one or more additional factors. Thus, as usedherein, the term “based on” should be understood to be equivalent to theterm “based at least on.”

Numerous example embodiments are described as follows. It is noted thatany section/subsection headings provided herein are not intended to belimiting. Example embodiments are described throughout this document,and any type of embodiment may be included under any section/subsection.Furthermore, example embodiments disclosed in any section/subsection maybe combined with any other embodiments described in the samesection/subsection and/or a different section/subsection in any manner.

II. Example Implementations

Messaging systems, such as chat bots, may be utilized in a variety ofcontexts to simulate natural conversations with humans. In typicalsystems, a topic is selected that corresponds to the user query. Basedon the selected topic, the chat bot provides a response to the user.

In existing systems, a user query may unintentionally align withmultiple topics. For example, a user may input a phrase to a chat bot,but due to the functioning of the chat bot, several possible topicsrelating to the phrase are identified. These overlapping topics resultin additional dialog between the user and the chat bot to determine anappropriate topic of interest. For instance, a chat bot may need to aska user one or more clarifying questions to identify an appropriate topicfor the conversation, such as a question asking the user which topic theuser is interested in (e.g., a “Did you mean” question). Theseoverlapping topics therefore can result in poor overall performance andusability of the chat bot.

Embodiments described herein address these and other issues by providingmethods, systems, apparatuses, and computer program products fordetecting overlapping topics in a messaging system. In an examplesystem, a phrase representation generator is configured to receive aplurality of trigger phrases, where each trigger phrase is configured totrigger a bot that receives the trigger phrase to select a correspondingtopic for conversation. The phrase representation generator is alsoconfigured to generate, for each trigger phrase, a vectorrepresentation. A topic overlap detector is configured to generatemeasures of similarity based at least on the vector representations,where each measure of similarity represents a degree of similaritybetween a respective pair of vector representations. The topic overlapdetector detects a topic overlap based on a pair of vectorrepresentations having a measure of similarity above a similaritythreshold, where the topic overlap indicates two trigger phrases thatare overlapping. The topic overlap detector provides the topic overlapto an authoring tool that comprises one or more interactive elements toenable a user to change at least one of the two trigger phrases that areoverlapping. In this manner, authors of bots (e.g., chat bots) may seeand resolve overlapping topics, resulting in improved performance of themessaging system. In other words, authors may use an authoring tool asdescribed herein to further define boundaries between topics so thattriggering of topics for conversation by a bot is more precise.

For instance, a messaging system may include a conversational artificialintelligence (AI) system in which a customer (e.g., an author or acitizen developer) can create, remove, or modify topics forconversation, where each topic may be defined by one or more triggerphrases. During a chat session between a user and conversational AIsystem, the system decides which of the defined topics best aligns withthe user dialog. In an example system where topic authoring is openended (e.g., an author defines the topics and/or trigger phrases thatlead to selection of the topic), overlapping topics may be present. Forexample, one topic may be “Locations” that include “Sesame Street,” andanother topic “Television Shows” that also includes “Sesame Street.”These topic definitions could result in an overlap that causes anambiguity for the conversational AI system that will need to be resolvedduring a conversation session if not addressed in advance. Moreover, bythe design of the system, cases of overlap are not necessarily exactstring matches, but may also be synonyms or otherwise semanticallysimilar phrases. As described in greater detail below, techniquesdescribed herein enable an author to view topic overlaps and resolvesuch overlaps to improve the functioning of the conversational AIsystem.

Identifying overlapping topics in a messaging environment as describedherein has numerous advantages, including but not limited to overallimprovement of the functioning of a messaging tool, such as aconversational AI system. For instance, by identifying overlappingtopics and enabling authors to disambiguate topics with trigger phrasesthat are similar, a conversational AI system may better simulate humanconversation by reducing the likelihood that the system asks aclarifying question or questions to identify the particular topic thatthe user is interested in. As these techniques enable more accurateparsing of user queries by identifying overlapping topics in advance,the conversational AI system may better and more efficiently interactwith users. As a result, techniques described herein may provideimprovements to the conversational AI system itself.

Furthermore, example embodiments described herein also improve thefunctioning of the computing systems and/or networks on which aconversational AI system is receiving or sending messages. For instance,because overlapping topics may be identified and resolved in advance(e.g., before a conversation begins between a user and theconversational AI system), additional clarifying questions (e.g., aquestion asking the user which topic the user is interested in) need notbe generated and asked by the AI system. Such a reduction in these typesof questions reduces the number of processing cycles used by thecomputing device(s) on which the conversational AI system operates andthe user’s computing device that receives and presents those messagesfrom the AI system. Furthermore, reducing the need for these additionalclarifying questions also reduces the amount of memory needed to storeadditional message information as well as the amount of data transmittedover a network between the AI system and the user. As a result,utilization of processing resources, memory resources, and networkingresources may be improved in accordance with techniques describedherein.

Example embodiments are described as follows for systems and methods fordetecting overlaps in a messaging system (e.g., a conversational AIsystem). For instance, FIG. 1 shows a block diagram of a messagingsystem, according to an example embodiment. As shown in FIG. 1 , system100 includes a computing device 102, a computing device 108, and acomputing device 118. Computing device 102 includes an agent interactioninterface 104. Computing device 108 includes a conversational AI system110. Conversational AI system 110 includes a virtual agent 112, anoverlap detection system 114, and a testing interface 116. Computingdevice 118 includes a user interface 120. User interface 120 includes anauthoring tool 122. Computing device 102, computing device 108, andcomputing device 118 may be communicatively coupled by a network 106. Anexample computing device that may incorporate the functionality ofcomputing device 102, computing device 108, and computing device 118 (orany subcomponents therein, whether or not illustrated in FIG. 1 ) isdescribed below in reference to FIG. 8 . It is noted that system 100 maycomprise any number of devices, including those illustrated in FIG. 1and optionally one or more further devices or components not expresslyillustrated. System 100 is further described as follows.

Network 106 may include one or more of any of a local area network(LAN), a wide area network (WAN), a personal area network (PAN), acombination of communication networks, such as the Internet, and/or avirtual network. In example implementations, computing device 102,computing device 108, and computing device 118 communicate via network106. In an implementation, any one or more of computing device 102,computing device 108, and computing device 118 communicate over network106 via one or more application programming interfaces (API) and/oraccording to other interfaces and/or techniques. Computing device 102,computing device 108, and computing device 118 may each include at leastone network interface that enables communications with each other.Examples of such a network interface, wired or wireless, include an IEEE802.11 wireless LAN (WLAN) wireless interface, a WorldwideInteroperability for Microwave Access (Wi-MAX) interface, an Ethernetinterface, a Universal Serial Bus (USB) interface, a cellular networkinterface, a Bluetooth™ interface, a near field communication (NFC)interface, etc. Further examples of network interfaces are describedelsewhere herein.

Computing device 102 includes one or more computing devices of one ormore users (e.g., individual users, family users, enterprise users,governmental users, etc.) that each comprise one or more applications,operating systems, virtual machines, storage devices, etc. that may beused to interact with virtual agent 112. Computing device 102 may be anytype of stationary or mobile computing device, including a mobilecomputer or mobile computing device (e.g., a Microsoft ® Surface®device, a personal digital assistant (PDA), a laptop computer, anotebook computer, a tablet computer such as an Apple iPad™, a netbook,etc.), a mobile phone (e.g., a cell phone, a smart phone such as anApple iPhone, a phone implementing the Google® Android™ operatingsystem, a Microsoft Windows® phone, etc.), a wearable computing device(e.g., a head-mounted device including smart glasses such as Google®Glass™, Oculus Rift® by Oculus VR, LLC, etc.), or other type ofstationary or mobile device. Computing device 102 is not limited to aphysical machine, but may include other types of machines or nodes, suchas a virtual machine. Computing device 102 may interface with othercomponents illustrated in FIG. 1 through APIs and/or by othermechanisms.

Agent interaction interface 104 may comprise any user interface throughwhich interaction with virtual agent 112 may be carried out. Inexamples, agent interaction interface 104 may comprise an application(e.g., a web browser, a chat application, a text-messaging application,an email application, etc.), an add-on or plug-in to an application, aservice that is accessed or triggered upon performing an action oncomputing device 102 (e.g., visiting a website), or an application orservice that is accessed on computing device 102 in other ways. Agentinteraction interface 104 may comprise one or more interactive userinterface (UI) elements that enable user interaction with a virtualagent 112. Examples of such UI elements include, but are not limited to,a text input portion, a message displaying portion, drop-down menu, oneor more radio or other selectable buttons, icons, a listing, or other UIelements that enable the transmission and/or receipt of messages toand/or from another entity, such as virtual agent 112 that may comprisea chat bot.

For instance, agent interaction interface 104 may transmit one or moreuser-generated phrases (e.g., user queries or other interactions)inputted therein to virtual agent 112. In some examples, agentinteraction interface 104 may comprise an interface (e.g., an interfaceassociated with an organization) via which a user may provide userqueries (e.g., customer support requests, etc.) to virtual agent 112.Agent interaction interface 104 may also be configured to receive one ormore responses from virtual agent 112 in response to the user queries,such as messages, purchase confirmations, booking confirmations, emails,etc. These examples are only illustrative, and other types ofcommunication between a user and another entity are contemplated.

Computing device 108 comprises one or more computing devices, servers,services, local processes, remote machines, web services, home assistantdevices, virtual assistants, gaming consoles, entertainment systems,etc. for managing the messaging between one or more virtual agents(e.g., chat bots) and one or more users accessing agent interactioninterface 104. For instance, computing device 108 may comprise a localcomputing device, a server coupled to an organization’s network, aremotely located server, a collection of computing devices such as anetwork-accessible server (e.g., a cloud computing server network), orany other device or service that may manage the messaging betweenentities shown in FIG. 1 (and/or other entities not expressly shown).

Conversational AI system 110 comprises a messaging or othercommunication system that is semi-automated or fully automated. In someimplementations, conversational AI system 110 may be configured toreceive a query or other interaction from a user (e.g., user of agentinteraction interface 104) and provide a response to agent interactioninterface 104. As shown in FIG. 1 , conversational AI system 110includes a virtual agent 112, an overlap detection system 114, and atesting interface 116.

Virtual agent 112 may comprise one or more services in which anautomated or semi-automated interaction service may be implemented, suchas an artificial intelligence software program that may employ naturallanguage processing techniques to simulate human interaction with endusers (e.g., responding with information in a messaging interface, carryout one or more requested tasks, look up order or account information,etc.). In example implementations, virtual agent 112 may be configuredto simulate human interaction via applications of one or more languagemodels to parse received user queries and determine an appropriatemessage in response. In some example embodiments, virtual agent 112comprises a chat bot for simulating conversation with a user. Inimplementations, virtual agent 112 may be configured to select a topicfor conversation by identifying a trigger phrase that matches (e.g.,identically or semantically) a received user query. In response toidentifying a matching trigger phrase, virtual agent 112 may determine atopic for conversation with the user, and provide appropriate messagingin response (e.g., assisting with placing an order, looking up areservation, etc.). Virtual agent 112 may comprise any interactionmethod, including but not limited to textual interaction (e.g., two-waymessaging) and/or audible interaction (e.g., automated voice or speechinteractions such as in automated telephone systems). It is also notedthat disclosed techniques may be implemented in other contexts, such asparsing search queries (e.g., inputted to a website) that are matched totriggers in order to return an appropriate set of results (e.g., pages,categories, products, etc.) to a user.

Overlap detection system 114 is configured to process authored topics,which may include trigger phrases, and detect similarities between them(as described in greater detail below). For instance, overlap detectionsystem 114 may be configured to process one or more trigger phrasesassociated with the topics, where such processing comprises generating arepresentation of each trigger phrase (e.g., generating a vectorrepresentation using one or more language models). Similarities betweentrigger phrases are detected based on a measure of similarity that isdetermined based at least on their representations. For instance,overlap detection system 114 may identify topics with trigger phrasesthat have a high degree of similarity with each other as overlappingtopics (e.g., topics that have trigger phrases with similarities thatblur the boundaries between topics), and store an indication of each ofthose overlapping topics (also referred to herein as overlapping topicpairs). Overlap detection system 114 may also generate a similarityscore and/or a ranking based thereon for the overlapping topics.Authoring tool 122 (as described in greater detail below) may provide anidentification of the topics that overlap with at least one other topic,a similarity score associated with the overlapping topics, and/or aranking (e.g., a ranking of each topic by an associated similarity scorewith another topic). In some implementations, authoring tool 122identifies one or more trigger phrases from the overlapping topics thatcaused the detection of the overlap. The author may be provided theability, via authoring tool 122, to rewrite (e.g., edit) or remove anyone or more of the trigger phrases that caused the overlap between thetopics.

In this manner, overlap detection system 114 may enable authors to viewand disambiguate overlapping topics, thereby reducing the need forvirtual agent 112 to ask clarifying questions to users of agentinteraction interface 104 in attempting to trigger a topic forconversation. As a result, improvements in the overall accuracy andusability of conversational AI system 110 may be achieved. Additionaldetails relating to the operation of overlap detection system 114 willbe described in greater detail below.

Testing interface 116 comprises a testing environment in which an authormay provide test or sample queries to virtual agent 112 to evaluate itsperformance. For instance, testing interface 116 may enable an author orother individual to engage in a simulated conversation with virtualagent 112. The conversation may comprise a test query that includes atrigger phrase provided to virtual agent 112 to determine whether thetest query results in virtual agent 112 identifying a plurality oftopics for conversation, or whether the test query results in virtualagent 112 identifying only a single topic for conversation. Forinstance, where a test query indicates that virtual agent 112 identifiesmore than one topic for conversation, authoring tool 122 may be utilizedto make a change to one more or trigger phrases to reduce the likelihoodthat virtual agent 112 identifies more than one topic for the same orsimilar queries. Testing interface 116 may be provided for presentationand/or interaction in authoring tool 122 or elsewhere in UI 120 as awindow, pane, overlay, etc.

Computing device 118 includes one or more computing devices thatcomprise one or more applications, operating systems, virtual machines,storage devices, etc. that may be used to manage the operation and/orfunctionality of conversational AI system 110. Computing device 118 maybe any type of stationary or mobile computing device, including a mobilecomputer or mobile computing device (e.g., a Microsoft ® Surface®device, a personal digital assistant (PDA), a laptop computer, anotebook computer, a tablet computer such as an Apple iPad™, a netbook,etc.), a mobile phone (e.g., a cell phone, a smart phone such as anApple iPhone, a phone implementing the Google® Android™ operatingsystem, a Microsoft Windows® phone, etc.), a wearable computing device(e.g., a head-mounted device including smart glasses such as Google®Glass™, Oculus Rift® by Oculus VR, LLC, etc.), or other type ofstationary or mobile device. Computing device 118 is not limited to aphysical machine, but may include other types of machines or nodes, suchas a virtual machine. Computing device 118 may interface with othercomponents illustrated in FIG. 1 through APIs and/or by othermechanisms.

User interface (UI) 120 includes one or more UI elements that enablecustomization and/or management of one or more features ofconversational AI system 110. For instance, UI 120 may comprise aninterface that includes one or more interactive UI elements that enablethe configuration (e.g., by a user and/or programmatically) of aspectsof conversational AI system 110 as described herein. In implementations,UI 120 may be accessed via a web browser by navigation to a web page,via an application stored on computing device 118, or other similartechniques. These examples are only illustrative, and UI 120 may provideany type, number, and/or arrangement of elements to manage and/orcustomize the manner in which conversational AI system 110 operates.

Authoring tool 122 may comprise an interface that provides a user withthe ability to author and/or modify various aspects of conversational AIsystem 110. For instance, authoring tool 122 may comprise one or more UIelements for viewing, rewriting (e.g., modifying), creating and/orremoving trigger phrases, topics, prompts, responses, or any otheraspect that defines how virtual agent 112 operates. A trigger phraseincludes one or more sets of words that, if included in a user query,triggers virtual agent 112 to select a particular topic for conversationfor responding. A topic may include any subject or category for whichvirtual agent 112 can engage in simulated interaction. A single topicmay be associated with a plurality of trigger phrases that, whenreceived by virtual agent 112, are configured to trigger the virtualagent to select the topic for conversation. As an illustrative example,the trigger phrases “suspend subscription” or “cancel subscriptionrenewal” could be assigned to a topic of “cancel subscription.” Anynumber of trigger phrases and/or topics may be defined via authoringtool 122.

It is also noted that authoring tool 122 need not receive authoringinformation from users. In some other implementations, authoring tool122 may be configured to import authoring information (e.g., triggerphrases, topics, and/or any other authoring information that defines howvirtual agent 112 operates) from a file, another computing device, orvia any other means. Furthermore, authoring tool 122 may provide onemore UI elements to view, rewrite, create, and/or remove any authoringinformation that defines how virtual agent 112 operates (e.g., rewritingexisting topics or trigger phrases, adding new ones, etc.). Stillfurther, authoring tool 122 may comprise one or more UI elements thatenable an author to selectively enable or disable certain features ofconversational AI system 110, such as overlap detection system 114 (orany of the features contained therein).

In example embodiments, authoring tool 122 displays informationgenerated by overlap detection system 114. For instance, authoring tool122 may identify a ranking of overlapping topics, a similarity score(which may also be referred to as a severity score) associated withoverlapping topics, a set of trigger phrases (e.g., descriptive phrasesintended to trigger selection of a topic by a bot) that causes certaintopics to overlap, and one or more UI elements that enables a user torewrite and/or remove trigger phrases that cause topics to overlap. Thesimilarity score may be based on a range of possible values (e.g.,numerical values or percentages, grades, categories, etc.) with lowvalues (e.g., 1 on a scale of 1-100) representing a low level ofsimilarity with another topic and high values (e.g., 100 on the samescale of 1-100) representing a high level of similarity with anothertopic. In this manner, authoring tool 122 and/or overlap detectionsystem 114 may enable a user to see and resolve overlapping topics,thereby improving the performance of virtual agent 112.

It is noted that implementations are not limited to the illustrativearrangement shown in FIG. 1 . For instance, computing device 102,computing device 108, and/or computing device 118 need not be separateor located remote from each other. In some examples, any one or more ofcomputing device 102, computing device 108, and/or computing device 118(or any subcomponents therein) may be located in or accessible via thesame computing device or distributed across a plurality of devices. Forinstance, techniques described herein may be implemented in a singlecomputing device. Furthermore, system 100 may comprise any number ofcomputing devices, virtual agents, networks, servers, or any othercomponents coupled in any manner.

Overlap detection system 114 may operate in various ways to identifyoverlapping topics in a messaging environment. For instance, FIG. 2shows a block diagram of a system for detecting overlapping topics in amessaging system, according to an example embodiment. As shown in FIG. 2, system 200 includes an example implementation of overlap detectionsystem 114 and authoring tool 122. Overlap detection system 114 includesa set of trigger phrases 202, a language model 204, a phraserepresentation generator 206, an overlap detector 208, a similaritythreshold 210, and one or more topic overlaps 212. Each of thesecomponents of system 200 are described in further detail as follows.

Authoring tool 122 may be used to define a plurality of trigger phrases202 that are provided 214 to overlap detection system 114, where eachtrigger phrase may be associated with a topic. As described herein, atrigger phrase of the plurality of trigger phrases 202 may include aword or phrase that, when received by virtual agent 112, configured totrigger virtual agent 112 to select a corresponding topic forconversation. Trigger phrases 202 may comprise one or more text files,listings, databases, tables, or any other suitable data structures inwhich each trigger phrase is mapped to a corresponding topic. Inexamples, a topic may have a plurality of trigger phrases associatedtherewith. Trigger phrases 202 may be authored manually by a user (e.g.,via authoring tool 122), imported from a set of pre-authored triggerphrases, or any combination thereof.

In examples, the set of trigger phrases 202 may comprise any number oftrigger phrases related to any subject, such as customer service,e-commerce, travel reservations, multimedia, technology, etc.Accordingly, when a user of agent interaction interface 104 provides aquery to initiate communication with virtual agent 112, virtual agent112 may parse the user query (e.g., by generating a vectorrepresentation as described herein) to identify a topic associated withthe user query based on the set of trigger phrases 202. For instance, ifa user query comprises the words “stop subscription,” virtual agent 112may identify a topic associated with cancelling a subscription fromtrigger phrases 202. It is noted that virtual agent 112 need not observea precise match between the user query and a trigger phrase of triggerphrases 202 to identify and/or select a topic for conversation. Rather,virtual agent 112 may identify similar words or phrases (e.g., synonymsor semantically similar phrases) based on the user query and identifyand/or select a particular topic for conversation based on the similarwords or phrases to the user query and trigger phrase 202. For example,virtual agent 112 may generate a vector representation of the receivedquery, and determine a similarity of this vector representation withvector representations corresponding to each of trigger phrases 202 toidentify a matching trigger phrase and/or topic (e.g., by identifying atrigger phrase with a high degree of similarity to the user query).

In some examples, virtual agent 112 may identify a plurality of possibletopics for conversation based on the user query. For instance, virtualagent 112 may determine that the user query aligns with two or moretrigger phrases of the set of trigger phrases 202, and therefore theuser query may be associated with multiple topics. In such a scenario,virtual agent 112 may transmit a clarifying question (e.g., a “Did youmean” question) to agent interaction interface 104 to identify aparticular topic for conversation from among the plurality of possibletopics.

Overlap detection system 114 may reduce the likelihood of suchclarifying questions in accordance with techniques described herein. Forinstance, phrase representation generator 206 may obtain or receive 216the plurality of trigger phrases 202 and generate a representation foreach trigger phrase contained therein. The representation may includeany type of value that represents a meaning (e.g., a semantic meaning)of trigger phrase, such as a vector or other numerical representation(e.g., an array). In some implementations, the representation maycomprise a vector in a multi-dimensional space. In example embodiments,phrase representation generator 206 may generate a representation foreach trigger phrase by applying language model 204 to each triggerphrase.

Language model 204 comprises one or more language models that may beused to generate a vector or other representation for a word or phrase.In some examples, language model 204 comprises an embedding modelconfigured to generate an embedding. An embedding model may comprise adeep-learning model that may be configured to map a word or sequence ofwords to a numerical value, such as a multi-dimensional vector. Theembedding model may be trained based on an algorithm that utilizeslanguage data that comprises the usage of words in a given language,such as books, academic literature, dictionaries, encyclopedias, dataavailable on the Internet, newspapers, other language models, and/or anyother language data. In some implementations, the embedding model may betrained based on millions or billions of word or word combinations andcomprise hundreds or even thousands of dimensions. It is noted thatalthough certain examples are discussed herein that include generatingan embedding based on an embedding model, embodiments are not limited tothese examples. Phrase representation generator 206 may be configured togenerate any other suitable type of representations (e.g., vectors) foreach trigger phrase of the plurality of trigger phrases 202.

Examples of algorithms that may be used to train and/or generate alanguage model 204 include, but are not limited to Word2Vec, designed byGoogle LLC, Global Vector (GloVe) designed by Stanford University, andfastText designed by Facebook, Inc. Furthermore, language model 204 maybe trained using various types of learning techniques as will beappreciated to those skilled in the relevant arts, including but notlimited to skip-gram, co-occurrence learning, negative sampling, etc.These examples are illustrative only and may include other algorithmsfor training language model 204, including any other natural languageprocessing (NLP) or natural language understanding (NLU) methodsappreciated to those skilled in the relevant arts.

Language model 204 may be generated in various forms. For instance,language model 204 may be generated according to a suitable supervisedand/or unsupervised machine-learning algorithm. For instance, languagemodel 204 may be generated by implementing a vector space learningalgorithm to generate the embedding model as a vector space model. As avector space model, language model 204 may represent individual words orsequences of words in a continuous vector space (e.g., amulti-dimensional space), where similar words or sequences of words aremapped to nearby points or are embedded near each other. Furthermore, anartificial neural network learning algorithm may be used to generateand/or train language model 204 as a neural network that is aninterconnected group of artificial neurons. The neural network may bepresented with word or sequence of words to identify a representation ofthe inputted word or sequences of words. Language model 204 could beimplemented using any suitable neural network architecture.

When phrase representation generator 206 applies a trigger phrase oftrigger phrases 202 to language model 204, phrase representationgenerator 206 generates a vector representation of the trigger phrase.In this way, language model 204 may be applied 218 by phraserepresentation generator 206 to generate a vector representation for asingle word, a word span that comprises several words, or an entiresentence (e.g., by combining one or more representations for individualwords in the trigger phrase) present in each trigger phrase.

Overlap detector 208 may be configured to generate measures ofsimilarity based at least on the vector representations generated by andobtained 220 from phrase representation generator 206. In examples, eachmeasure of similarity may represent a degree of similarity between arespective pair of vector representations. In other words, each measureof similarity may be indicative of how similar (e.g., a level or degreeof semantic similarity) one trigger phrase of trigger phrases 202 is toanother trigger phrase of trigger phrases 202 based on their respectivevector representations. Based at least on the measures of similarity,overlap detector 208 may be configured to identify pairs of triggerphrases that overlap. Accordingly, overlap detector 208 may determine ameasure of similarity between a vector representation of one triggerphrase with or more vector representations of other trigger phrases, andstore an indication for each pair of vector representations that has asimilarity score above similarity threshold 210. Pairs of vectorrepresentations that have a similarity score above a threshold may beidentified as overlapping. In examples, similarity threshold 210 may beobtained 222 by overlap detector 208 and comprise a minimum measure ofsimilarity between two or more phrase representations to identify thetrigger phrases as overlapping. Similarity threshold 210 may bepredefined in some implementations, or may be customizable by a user(e.g., via user interface 120) in other implementations. Overlappingtopics may include any topics that has at least one trigger phrase thatoverlaps with a trigger phrase of another topic (i.e., a pair of triggerphrases that has a similarity score above a threshold).

Accordingly, for each pair of trigger phrases comprising vectorrepresentations that have a measure of similarity above similaritythreshold 210, overlap detector 208 may be configured to detect thepresence of a topic overlap, and store 224 an indication of the overlapin a set of topic overlaps 212. In some implementations, the indicationmay also comprise the similarity score or other score indicative of alevel or degree of similarity between trigger phrases. Overlap detectormay be configured to generate a ranking or prioritization based on sucha score and store such information as part of topic overlaps 212, suchthat trigger phrases with the highest similarity score appear at the topof a list presented in authoring tool 122 and/or may be selectivelyranked by users of the tool.

In some implementations, topic overlaps 212 may store information foreach trigger phrase that is overlapping with another trigger phraseand/or each topic that has an overlapping trigger phrase with anothertopic. For instance, topic overlaps 212 may store an indication that afirst topic comprising a first trigger phrase is overlapping (e.g.,similar) with a second topic comprising a second trigger phrase, basedat least on the phrase representations of the first and second triggerphrases having a similarity score above similarity threshold 210.

Topic overlaps 212 may be presented or provided 226 to authoring tool122 such that a user of authoring tool 122 may view each topic (and/orits associated trigger phrase) that is similar with another topic(and/or its associated trigger phrases). As noted earlier, authoringtool 122 may also be configured to rank the topic overlaps based atleast on the similarity score associated with each topic overlap,enabling a user to prioritize overlapping topic pairs that may be moreproblematic than others. In implementations, authoring tool 122 mayprovide one or more interactive UI elements identifying each triggerphrase causing the topic overlap (e.g., a trigger phrases with a vectorrepresentation that has a measure of similarity above similaritythreshold 210 with a vector representation of a trigger phrase ofanother topic). Authoring tool 122 may also provide one or moreinteractive UI elements, that when activated, enable a user to changeone or more of the overlapping trigger phrases, such as enabling a userto modify characters or words in a trigger phrase or remove a triggerphrase from an overlapping topic altogether.

In some implementations, overlap detector 208 may be configured toupdate the set of topic overlaps 212 each time authoring tool 122 isused to makes a change to trigger phrases 202, such as providing a newtrigger phrase, rewriting (e.g., editing) an existing trigger phrase, orremoving (e.g., deleting) a trigger phrase. The update may occurautomatically (e.g., in response to a change received in authoring tool122), or may be triggered in response to activation of an interactiveelement in authoring tool 122 (e.g., an update icon). The set of topicoverlaps 212 may also be updated at any other time or in accordance withany interval, such as at a predetermined period of time after a changeis made to trigger phrases 202, and/or periodically. In someimplementations, overlap detector 208 may update topic overlaps 212 inreal-time or near-real time as a user is authoring a trigger phrase inauthoring tool 122, such as by presenting, in authoring tool 122, areal-time notification of a topic overlap as the user is typing and/oras the user has completed an input of a trigger phrase for a topic.

In this manner, a citizen developer (e.g., a user that does not have anyspecialized training or expertise in the development of virtual agent112) can readily determine, based on trigger phrases authored, whethersuch phrases have similarities that would hinder the performance ofvirtual agent 112. Techniques described herein may bring transparency tothe authoring process and conversional AI used in dialog (e.g., byidentifying types of queries that may make it difficult for virtualagent 112 to select a topic).

Overlap detection system 114 may operate in various ways to detectoverlapping topics in messaging systems. For instance, overlap detectionsystem 114 may operate according to FIG. 3 . FIG. 3 shows a flowchart300 of a method for detecting overlapping topics in a messaging system,in accordance with an example embodiment. For illustrative purposes,flowchart 300 and overlap detection system 114 are described as followswith respect to FIGS. 1 and 2 . While example embodiments are describedwith respect to components of system 100, system 200, and flowchart 300,these examples are illustrative.

Flowchart 300 begins with step 302. In step 302, a plurality of triggerphrases is received, each trigger phrase being configured to trigger abot that receives the trigger phrase to select a corresponding topic forconversation. For instance, with reference to FIG. 2 , phraserepresentation generator 206 may be configured to receive triggerphrases 202, as described herein. The set of trigger phrases 202 may bereceived as one or more files (e.g., as raw text, tables, spreadsheets,etc.) or in any other format. Trigger phrases 202 may comprise a set oftrigger phrases that define the operation of a bot (e.g., virtual agent112). Each trigger phrase of trigger phrases 202 may be configured totrigger a bot that receives the trigger phrase to select a correspondingtopic for conversation. For example, a trigger phrase of “suspendsubscription” (or a semantically similar phrase), when provided to abot, causes the bot to select a corresponding topic of “cancelsubscription,” and initiate a conversation with a user to cancel theirsubscription. Each trigger phrase that is authored as part ofconversational AI system 110 may be received by phrase representationgenerator 206.

In step 304, a vector representation is generated for each triggerphrase of the plurality of trigger phrases. For instance, with referenceto FIG. 2 , phrase representation generator 206 may be configured togenerate a vector representation, for each trigger phrase of triggerphrases 202, as described herein. In examples, phrase representationgenerator 206 may be configured to apply language model 204 to eachtrigger phrase to generate each vector representation. In someimplementations, the vector representation may comprise an embeddinggenerated by applying language model 204. In this manner, phraserepresentation generator 206 may be configured to generate a pluralityof vector representations, each vector representation corresponding to atrigger phrase of the set of trigger phrases 202.

In step 306, measures of similarity are generated based at least on thevector representations, each measure of similarity representing a degreeof similarity between a respective pair of vector representations. Forinstance, with reference to FIG. 2 , overlap detector 208 may beconfigured to generate measures of similarity based at least on thegenerated vector representations, as described herein. As describedabove, each measure of similarity may represent or indicate a degree ofsimilarity (e.g., semantic similarity) between a pair of vectorrepresentations. For example, overlap detector 208 may generate ameasure of similarity (e.g., a cosine similarity) between a first vectorrepresentation corresponding to a first trigger phrase and a secondvector representation corresponding to a second trigger phrase in theset of trigger phrases 202. In some implementations, the measures ofsimilarity may be generated for vector representations corresponding totrigger phrases belonging to different topics (e.g., a similaritybetween each vector representation of one topic to each vectorrepresentation of other topics). In this manner, overlap detector 208may generate a set of measures of similarity, with each measure ofsimilarity indicating a degree of similarity between different vectorrepresentations.

In step 308, a topic overlap is detected based on a pair of vectorrepresentations having a measure of similarity above a similaritythreshold, the topic overlap indicating two trigger phrases that areoverlapping. For instance, with reference to FIG. 2 , overlap detector208 may be configured to detect a topic overlap based on a pair ofvector representations having a measure of similarity above similaritythreshold 210, as described herein. The topic overlap may indicate twotrigger phrases (i.e., the two trigger phrases that correspond to thevector representations in this pair of vector representations) that areoverlapping. A determination of a topic overlap may therefore indicatethat the two topics comprise trigger phrases that have a semanticsimilarity above a threshold, such that if either trigger phrase isprovide to a bot, the bot may identity both corresponding topics aspossible topics for conversation. In other words, if the bot is providedwith either of the two trigger phrases that are overlapping as detectedby overlap detector 208, the bot may be configured to identify twotopics for conversation (which, if unresolved, can lead to additionalinteraction between the user and the bot as described herein).Information associated with each topic overlap (e.g., a topic name,trigger phrase(s), similarity scores, etc. associated with the detectedtopic overlap) may be stored as a set of topic overlaps 212. Whileexamples are described herein that two topics or trigger phrases mayoverlap, any number of topics or trigger phrases may be identified asoverlapping with each other based on their semantic similarity.

In step 310, the topic overlap is provided to an authoring tool thatcomprises one or more interactive elements to enable a user to change atleast one of the two trigger phrases that are overlapping. For instance,with reference to FIG. 2 , overlap detector 208 may be configured toprovide the topic overlap (e.g., individually or as a set of topicoverlaps 212) to authoring tool 122, as described herein. Authoring tool122 may comprise one or more interactive elements, that when activated,enable a user to change (e.g., rewrite or remove) at least one of thetwo trigger phrases that are overlapping in the particular topicoverlap.

By identifying overlapping topics in the disclosed manner, changes maybe made to the authoring of a bot in a more efficient manner such thatthe bot exhibits an improved performance (e.g., more efficientlyidentifying topics of conversation associated with user queries, whichcan reduce the number of messages that need to be generated,transmitted, and/or stored) during interaction with users. Furthermore,because techniques described herein need not utilize actual user queriesto identify overlapping topics, improvements to the performance of thebot may be achieved during development or authoring of the bot, therebyimproving the likelihood of an optimally performing bot once deployed.

As discussed above, overlap detection system 114 may be configured toupdate similarity scores associated with a topic overlap. For instance,FIG. 4 shows a flowchart 400 of a method for receiving a change to anoverlapping trigger phrase, in accordance with an example embodiment. Inan implementation, the method of flowchart 400 may be implemented bycomponents of overlap detection system 114. FIG. 4 is described withcontinued reference to FIGS. 1 and 2 . Other structural and operationalimplementations will be apparent to persons skilled in the relevantart(s) based on the following discussion regarding flowchart 400, system100 of FIG. 1 , and system 200 of FIG. 2 .

Flowchart 400 begins with step 402. In step 402, a change to one of thetwo trigger phrases that are overlapping is received. For instance, withreference to FIG. 2 , authoring tool 122 may provide one or moreinteractive elements that may enable a user to change one or moretrigger phrases associated with a topic overlap (e.g., to resolve theoverlap). When a user interacts with the interactive elements to make achange to one or more overlapping trigger phrases, authoring tool 122may receive an indication of the change and provide the change to updatethe set of trigger phrases 202.

In step 404, an updated measure of similarity is generated based atleast on the changed trigger phrase. For instance, with reference toFIG. 2 , phrase representation generator 206 may be configured togenerate a vector representation as described herein for the one or moretrigger phrases that were changed via interaction with authoring tool122. In response to the generating a vector representation for eachchanged trigger phrase, overlap detector 208 may be configured togenerate a measure of similarity in a similar manner as describedherein. In some implementations, phrase representation generator 206 andoverlap detector 208 may configured to generate an updated measure ofsimilarity automatically (e.g., without further input from a user afterthe user provides the change to a trigger phrase in authoring tool 122),or manually (e.g., via an interactive element to generated one or moreupdated measures of similarity). In some implementations, once a changeis received to one of the two trigger phrases, a bot may be configuredto select a single topic for conversation when receiving a user querythat is the same or semantically similar to changed trigger phrase(whereas prior to the change being provided, the bot may identify aseveral topics for conversation due to the topic overlap being present).

In some examples, if the updated vector representation associated withthe changed trigger phrase still has a measure of similarity with avector representation of another trigger phrase that is above similaritythreshold 210, an updated measure of similarity based thereon may beprovided to authoring tool 122 to indicate that a topic overlap is stillpresent. In other examples, the updated measure of similarity may beprovided to authoring tool 122 for presentation even if the measure ofsimilarity is below similarity threshold 210.

As discussed above, overlap detection system 114 may be configured torank a plurality of topic overlaps. For instance, FIG. 5 shows aflowchart 500 of a method for ranking a plurality of topic overlaps, inaccordance with an example embodiment. In an implementation, the methodof flowchart 500 may be implemented by components of overlap detectionsystem 114. FIG. 5 is described with continued reference to FIGS. 1 and2 . Other structural and operational implementations will be apparent topersons skilled in the relevant art(s) based on the following discussionregarding flowchart 500, system 100 of FIG. 1 , and system 300 of FIG. 3.

Flowchart 500 begins with step 502. In step 502, a plurality of topicoverlaps is provided to the authoring tool, the plurality of topicoverlaps ranked based on measures of similarity corresponding to theplurality of topic overlaps. For instance, with reference to FIG. 2 ,overlap detector 208 may be configured to provide to authoring tool 122a plurality of topic overlaps (e.g., set of topic overlaps 212) alongwith a measure of similarity (e.g., a similarity score) corresponding toeach one, and/or a ranking based thereon. In this manner, authoring tool122 may be configured to present a ranked list of topic overlaps asdetected by overlap detector 208, ranked based on their correspondingmeasures of similarity. Such a ranking may enable users of authoringtool 122 to readily determine which topics have a greater amount ofoverlap and therefore can be prioritized for resolution.

FIG. 6 shows an illustrative user interface 600 for presentingoverlapping topic information, according to an example embodiment. Forinstance, as shown in FIG. 6 , UI 600 may comprise one exampleimplementation of authoring tool 122. In UI 600, an overlapping topicpane 602 is presented therein that contains a listing of eachoverlapping topic and a similarity score associated with eachoverlapping topic (e.g., as determined by overlap detection system 114).In one implementation, overlapping topic pane 602 of UI 300 may alsoshow, for each overlapping topic, a number of other topics (or triggerphrases) that overlap with the listed topic (or the listed topic’strigger phrases). As discussed above, the similarity score may bedetermined by overlap detection system 114 as it evaluates howsemantically similar trigger phrases of topics are to each other (e.g.,how much overlap is present between trigger phrases, either identicallyor semantically). A higher similarity score, as shown in overlappingtopic pane 602, may indicate that a particular topic has one or moretrigger phrases that are close to another topics trigger phrases.

As described above, overlapping topic pane 602 may be configured togroup together one or more trigger phrases belonging to a single topictogether, such that overlapping topic pane 602 provides a listing oftopics that overlap with other topics (which can be interacted with toobtain further information, such as trigger phrases that contributed tothe topic overlap being detected). Such an arrangement may enable anauthor to more efficiently navigate through authoring tool 122 toidentify topic that may need attention, thereby providing improvementsto the user interface in addition to other improvements noted herein.Displaying of topic information, however, is not required in allimplementations, and it is noted that overlapping topic pane 602 may beconfigured to display overlapping trigger phrases without displaying acorresponding topic name.

Similarity scores for an overlapping topic that comprises multipleoverlapping trigger phrases may be generated in various ways. Forinstance, the similarity scores for each overlapping topic may be basedon an average of the individual similarity scores for each pair ofoverlapping trigger phrases. In some examples, the average may comprisea weighted average (e.g., by weighting higher similarity scores moreheavily than lower similarity scores), which may further enable a userto more efficiently prioritize which topics may have overlaps that needto be resolved.

Overlapping topic pane 602 may include any arrangement of interactiveelements for an author to view, create, rewrite, and/or remove topics ortrigger phrases, whether or not expressly illustrated in FIG. 6 . Forinstance, overlapping topic pane 602 may include one or more UI elementsthat, when activated, sort the list of overlapping topics by similarityscore, topic name, number of trigger phrase overlaps, or any othermeans. Furthermore, when a particular score, topic, or number ofoverlaps is selected, overlapping topic pane 602 may be configured topresent additional information associated with the selected option, suchas identifying each trigger phrase for the particular topic and/or eachtrigger phrase of other topics that contributed to the identification ofthe topic overlap, additional details regarding how the similarity scorewas generated, etc. Overlapping topic pane 602 may also include otherelements or functions not shown, such as a testing pane (e.g., testinginterface 116) in which an author can submit a test query to virtualagent 112 to evaluate its performance, as described herein.

FIG. 7 shows an illustrative user interface 700 for presentingoverlapping topic information, according to an example embodiment. Forinstance, as shown in FIG. 7 UI 700 may comprise another exampleimplementation of authoring tool 122. In UI 700, an overlapping topicinformation pane 702 may be provided as a separate pane (e.g., as anadditional window, an overlay, etc.) providing additional details when aparticular overlapping topic in the list of overlapping topics isselected. For example, as shown in FIG. 7 , when a “Cancel subscription”topic is selected from the list of overlapping topics, additionaldetails are shown in overlapping topic information pane 702 that showany overlapping topics related to the “Cancel subscription” topic,including details relating to the trigger phrases of the overlappingtopics that contributed to the overlap being detected. In this example,a first trigger phrase “suspend subscription” associated with the“Cancel subscription” topic semantically overlaps with the triggerphrase “stop paper subscription” in the “Paper subscriptions” topic, andis thus presented in UI 700 as a pair of overlapping topics.

In this particular example, overlap detection system 114 has determinedthat such trigger phrases have a semantically similar relationship(e.g., the trigger phrases contain similar phrases, words, grammar,etc.) in accordance with techniques described herein. If leftunresolved, virtual agent 112 may not know which topic to select if auser query is received that contains either trigger phrase (or a phrasethat is semantically similar to either trigger phrase), which may causevirtual agent 112 to ask a follow-up question to the user to resolve theconfusion in order to identify the particular topic to open forconversation (e.g., a “What did you mean?” question may be asked to theuser).

In accordance with techniques described herein, identifying semanticallysimilar trigger phrases can enable a user to interact with authoringtool to improve the performance of virtual agent 112, such as byidentifying topics that themselves are similar and could be consolidatedto simplify the authoring process, rewriting or removing trigger phrasesthat contribute to an overlap being detected, or making any otherediting changes to topics and/or trigger phrases to make topics moredistinct from each other. As shown in UI 700, one or more selectableelements may be included that when activated enable an author to launchillustrative topic overlap details pane 702 that provides additionaldetails for a particular topic (e.g., where additional trigger phrasescan be added, phrases can be removed, phrases can be modified, etc.).Further, UI 700 includes one or more elements in the topic overlapdetails pane 702 in which changes to one or more overlapping triggerphrases can be made, such as a rewrite of the overlapping trigger phraseor removal of the trigger phrase. By providing such change abilities inthe topic overlap details pane, additional navigation within UI 700 toin order to make changes to overlapping trigger phrases may not benecessary, thereby simplifying and improving the overall user interfacefor authoring.

Topic overlap details pane 702 may include one more additionalinteractive elements to save changes such that they may be applied or tocancel such changes. In examples, after a save option is selected in thetopic overlap details pane, an overlapping status (which may include anyof the information shown in UI 600 and/or UI 700) may automatically berefreshed (e.g., overlap detection system 114 may perform an updatedbased on a revised set of trigger phrases that includes the author’smost recent changes). In some implementations, UI 600 and/or UI 700 mayinclude one or more elements for manually refreshing a topic overlapstatus.

The arrangement in FIGS. 6 and 7 are illustrative only, and any suitablearrangement of information generated and/or described herein iscontemplated. Further, UI 600 and/or UI 700 may contain one or moreadditional elements not shown. Examples of additional elements that canbe presented therein include, but are not limited to, one or moretables, graphs, charts, etc. that may indicate historical and/orstatistical information relating to prior user queries, such as a numberor percentage of queries that were matched to a topic, queries that werematched to a plurality of possible topics, and/or queries that did notmatch with any topics. Some of this information may also be provided tooverlap detector 206, such as to elevate or lower a similarity score fora pair of topics based on a number of observed chat sessions in whichboth of the topics were identified as possible topics for conversationby the bot.

III. Example Mobile and Stationary Device Embodiments

Computing device 102, agent interaction interface 104, computing device108, conversational AI system 110, virtual agent 112, overlap detectionsystem 114, testing interface 116, computing device 118, user interface120, authoring tool 122, trigger phrases 202, language model 204, phraserepresentation generator 206, overlap detector 208, similarity threshold210, topic overlaps 212, UI 600, UI 700, flowchart 300, flowchart 400,and/or flowchart 500 may be implemented in hardware, or hardwarecombined with one or both of software and/or firmware. For example,computing device 102, agent interaction interface 104, computing device108, conversational AI system 110, virtual agent 112, overlap detectionsystem 114, testing interface 116, computing device 118, user interface120, authoring tool 122, trigger phrases 202, language model 204, phraserepresentation generator 206, overlap detector 208, similarity threshold210, topic overlaps 212, UI 600, UI 700, flowchart 300, flowchart 400,and/or flowchart 500 may be implemented as computer programcode/instructions configured to be executed in one or more processorsand stored in a computer readable storage medium.

Alternatively, computing device 102, agent interaction interface 104,computing device 108, conversational AI system 110, virtual agent 112,overlap detection system 114, testing interface 116, computing device118, user interface 120, authoring tool 122, trigger phrases 202,language model 204, phrase representation generator 206, overlapdetector 208, similarity threshold 210, topic overlaps 212, UI 600, UI700, flowchart 300, flowchart 400, and/or flowchart 500 may beimplemented as hardware logic/electrical circuitry.

For instance, in an embodiment, one or more, in any combination, ofcomputing device 102, agent interaction interface 104, computing device108, conversational AI system 110, virtual agent 112, overlap detectionsystem 114, testing interface 116, computing device 118, user interface120, authoring tool 122, trigger phrases 202, language model 204, phraserepresentation generator 206, overlap detector 208, similarity threshold210, topic overlaps 212, UI 600, UI 700, flowchart 300, flowchart 400,and/or flowchart 500 may be implemented together in a system on a chip(SoC). The SoC may include an integrated circuit chip that includes oneor more of a processor (e.g., a central processing unit (CPU),microcontroller, microprocessor, digital signal processor (DSP), etc.),memory, one or more communication interfaces, and/or further circuits,and may optionally execute received program code and/or include embeddedfirmware to perform functions.

FIG. 8 depicts an exemplary implementation of a computing device 800 inwhich embodiments may be implemented. For example, computing device 102,agent interaction interface 104, computing device 108, conversational AIsystem 110, virtual agent 112, overlap detection system 114, testinginterface 116, computing device 118, user interface 120, authoring tool122, trigger phrases 202, language model 204, phrase representationgenerator 206, overlap detector 208, similarity threshold 210, topicoverlaps 212, UI 600, UI 700, flowchart 300, flowchart 400, and/orflowchart 500 (and/or any of the steps of flowcharts 300, 400, and/or500) may be implemented in one or more computing devices similar tocomputing device 800 in stationary or mobile computer embodiments,including one or more features of computing device 800 and/oralternative features. The description of computing device 800 providedherein is provided for purposes of illustration, and is not intended tobe limiting. Embodiments may be implemented in further types of computersystems, as would be known to persons skilled in the relevant art(s).

As shown in FIG. 8 , computing device 800 includes one or moreprocessors, referred to as processor circuit 802, a hardware accelerator803, a system memory 804, and a bus 806 that couples various systemcomponents including system memory 804 to processor circuit 802 andhardware accelerator 803. Processor circuit 802 and/or hardwareaccelerator 803 is an electrical and/or optical circuit implemented inone or more physical hardware electrical circuit device elements and/orintegrated circuit devices (semiconductor material chips or dies) as acentral processing unit (CPU), a microcontroller, a microprocessor,and/or other physical hardware processor circuit. Processor circuit 802may execute program code stored in a computer readable medium, such asprogram code of operating system 830, application programs 832, otherprograms 834, etc. Bus 806 represents one or more of any of severaltypes of bus structures, including a memory bus or memory controller, aperipheral bus, an accelerated graphics port, and a processor or localbus using any of a variety of bus architectures. System memory 804includes read only memory (ROM) 808 and random-access memory (RAM) 810.A basic input/output system 812 (BIOS) is stored in ROM 808.

Computing device 800 also has one or more of the following drives: ahard disk drive 814 for reading from and writing to a hard disk, amagnetic disk drive 816 for reading from or writing to a removablemagnetic disk 818, and an optical disk drive 820 for reading from orwriting to a removable optical disk 822 such as a CD ROM, DVD ROM, orother optical media. Hard disk drive 814, magnetic disk drive 816, andoptical disk drive 820 are connected to bus 806 by a hard disk driveinterface 824, a magnetic disk drive interface 826, and an optical driveinterface 828, respectively. The drives and their associatedcomputer-readable media provide nonvolatile storage of computer-readableinstructions, data structures, program modules and other data for thecomputer. Although a hard disk, a removable magnetic disk and aremovable optical disk are described, other types of hardware-basedcomputer-readable storage media can be used to store data, such as flashmemory cards, digital video disks, RAMs, ROMs, and other hardwarestorage media.

A number of program modules may be stored on the hard disk, magneticdisk, optical disk, ROM, or RAM. These programs include operating system830, one or more application programs 832, other programs 834, andprogram data 836. Application programs 832 or other programs 834 mayinclude, for example, computer program logic (e.g., computer programcode or instructions) for implementing any of the features of computingdevice 102, agent interaction interface 104, computing device 108,conversational AI system 110, virtual agent 112, overlap detectionsystem 114, testing interface 116, computing device 118, user interface120, authoring tool 122, trigger phrases 202, language model 204, phraserepresentation generator 206, overlap detector 208, similarity threshold210, topic overlaps 212, UI 600, UI 700, flowchart 300, flowchart 400,and/or flowchart 500 and/or further embodiments described herein.

A user may enter commands and information into computing device 800through input devices such as keyboard 838 and pointing device 840.Other input devices (not shown) may include a microphone, joystick, gamepad, satellite dish, scanner, a touch screen and/or touch pad, a voicerecognition system to receive voice input, a gesture recognition systemto receive gesture input, or the like. These and other input devices areoften connected to processor circuit 802 through a serial port interface842 that is coupled to bus 806, but may be connected by otherinterfaces, such as a parallel port, game port, or a universal serialbus (USB).

A display screen 844 is also connected to bus 806 via an interface, suchas a video adapter 846. Display screen 844 may be external to, orincorporated in computing device 800. Display screen 844 may displayinformation, as well as being a user interface for receiving usercommands and/or other information (e.g., by touch, finger gestures,virtual keyboard, etc.). In addition to display screen 844, computingdevice 800 may include other peripheral output devices (not shown) suchas speakers and printers.

Computing device 800 is connected to a network 848 (e.g., the Internet)through an adaptor or network interface 850, a modem 852, or other meansfor establishing communications over the network. Modem 852, which maybe internal or external, may be connected to bus 806 via serial portinterface 842, as shown in FIG. 8 , or may be connected to bus 806 usinganother interface type, including a parallel interface.

As used herein, the terms “computer program medium,” “computer-readablemedium,” and “computer-readable storage medium” are used to refer tophysical hardware media such as the hard disk associated with hard diskdrive 814, removable magnetic disk 818, removable optical disk 822,other physical hardware media such as RAMs, ROMs, flash memory cards,digital video disks, zip disks, MEMs, nanotechnology-based storagedevices, and further types of physical/tangible hardware storage media.Such computer-readable storage media are distinguished from andnon-overlapping with propagating signals and communication media (do notinclude propagating signals and communication media). Communicationmedia embodies computer-readable instructions, data structures, programmodules or other data in a modulated data signal such as a carrier wave.The term “modulated data signal” means a signal that has one or more ofits characteristics set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wireless media such as acoustic, RF,infrared and other wireless media, as well as wired media. Embodimentsare also directed to such communication media that are separate andnon-overlapping with embodiments directed to computer-readable storagemedia.

As noted above, computer programs and modules (including applicationprograms 832 and other programs 834) may be stored on the hard disk,magnetic disk, optical disk, ROM, RAM, or other hardware storage medium.Such computer programs may also be received via network interface 850,serial port interface 842, or any other interface type. Such computerprograms, when executed or loaded by an application, enable computingdevice 800 to implement features of embodiments discussed herein.Accordingly, such computer programs represent controllers of thecomputing device 800.

Embodiments are also directed to computer program products comprisingcomputer code or instructions stored on any computer-readable medium.Such computer program products include hard disk drives, optical diskdrives, memory device packages, portable memory sticks, memory cards,and other types of physical storage hardware.

IV. Example Embodiments

A system for detecting overlapping topics in a messaging system isdisclosed herein. The system includes at least one processor circuit;and at least one memory that stores program code configured to beexecuted by the at least one processor circuit, the program codecomprising: a phrase representation generator configured to: receive aplurality of trigger phrases, each trigger phrase being configured totrigger a bot that receives the trigger phrase to select a correspondingtopic for conversation, and generate a vector representation for eachtrigger phrase of the plurality of trigger phrases; and an overlapdetector configured to: generate measures of similarity based at leaston the vector representations, each measure of similarity representing adegree of similarity between a respective pair of vectorrepresentations; detect a topic overlap based on a pair of vectorrepresentations having a measure of similarity above a similaritythreshold, the topic overlap indicating two trigger phrases that areoverlapping; and provide the topic overlap to an authoring tool, whereinthe authoring tool comprises one or more interactive elements to enablea user to change at least one of the two trigger phrases that areoverlapping.

In one implementation of the foregoing system, the bot is configured toidentify two topics for conversation when provided with either triggerphrase of the two trigger phrases that are overlapping.

In another implementation of the foregoing system, the authoring tool isconfigured to receive a change to one of the two trigger phrases thatare overlapping, and the bot is configured to select a single topic forconversation when provided with the changed trigger phrase.

In another implementation of the foregoing system, each measure ofsimilarity is indicative of a degree of semantic similarity between afirst vector representation corresponding to a first trigger phrase anda second vector representation corresponding to a second trigger phrase.

In another implementation of the foregoing system, the system furtherincludes a testing interface configured to enable a simulatedconversation with the bot, the simulated conversation comprising aconversation to determine whether a trigger phrase provided to the botcauses the bot to identify a plurality of topics for conversation.

In another implementation of the foregoing system, the overlap detectoris configured to provide a plurality of topic overlaps to the authoringtool, the plurality of topic overlaps ranked based on measures ofsimilarity corresponding to the plurality of topic overlaps.

In another implementation of the foregoing system, the authoring tool isconfigured to receive a change to one of the two trigger phrases thatare overlapping, and the overlap detector is configured to automaticallygenerate an updated measure of similarity based at least on the changedtrigger phrase.

In another implementation of the foregoing system, the bot includes achat bot configured to simulate human conversation with a user.

In another implementation of the foregoing system, the vectorrepresentation includes an embedding generated by applying a languagemodel to each trigger phrase of the plurality of trigger phrases.

A method for detecting overlapping topics in a messaging system isdisclosed herein. The method includes receiving a plurality of triggerphrases, each trigger phrase being configured to trigger a bot thatreceives the trigger phrase to select a corresponding topic forconversation; generating a vector representation for each trigger phraseof the plurality of trigger phrases; generating measures of similaritybased at least on the vector representations, each measure of similarityrepresenting a degree of similarity between a respective pair of vectorrepresentations; detecting a topic overlap based on a pair of vectorrepresentations having a measure of similarity above a similaritythreshold, the topic overlap indicating two trigger phrases that areoverlapping; and providing the topic overlap to an authoring tool,wherein the authoring tool includes one or more interactive elements toenable a user to change at least one of the two trigger phrases that areoverlapping.

In one implementation of the foregoing method, providing either triggerphrase of the two trigger phrases that are overlapping to the bot causesthe bot to identify two topics for conversation.

In another implementation of the foregoing method, the method furtherincludes receiving a change to one of the two trigger phrases that areoverlapping via the authoring tool, wherein providing the changedtrigger phrase to the bot causes the bot to select a single topic forconversation.

In another implementation of the foregoing method, each measure ofsimilarity is indicative of a degree of semantic similarity between afirst vector representation corresponding to a first trigger phrase anda second vector representation corresponding to a second trigger phrase.

In another implementation of the foregoing method, the method furtherincludes providing a testing interface that enables a simulatedconversation with the bot, the simulated conversation comprising aconversation to determine whether a trigger phrase provided to the botcauses the bot to identify a plurality of topics for conversation.

In another implementation of the foregoing method, the method furtherincludes providing a plurality of topic overlaps to the authoring tool,the plurality of topic overlaps ranked based on measures of similaritycorresponding to the plurality of topic overlaps.

In another implementation of the foregoing method, the method furtherincludes receiving a change to one of the two trigger phrases that areoverlapping; and automatically generating an updated measure ofsimilarity based at least on the changed trigger phrase.

In another implementation of the foregoing method, the bot includes achat bot configured to simulate human conversation with a user.

In another implementation of the foregoing method, the vectorrepresentation includes an embedding generated by applying a languagemodel to each trigger phrase of the plurality of trigger phrases.

A computer-readable storage medium is disclosed herein. Thecomputer-readable storage medium has program instructions recordedthereon that, when executed by at least one processor of a computingdevice, perform a method, the method including: receiving a plurality oftrigger phrases, each trigger phrase being configured to trigger a botthat receives the trigger phrase to select a corresponding topic forconversation; generating a vector representation for each trigger phraseof the plurality of trigger phrases; generating measures of similaritybased at least on the vector representations, each measure of similarityrepresenting a degree of similarity between a respective pair of vectorrepresentations; detecting a topic overlap based on a pair of vectorrepresentations having a measure of similarity above a similaritythreshold, the topic overlap indicating two trigger phrases that areoverlapping; and providing the topic overlap to an authoring tool,wherein the authoring tool comprises one or more interactive elements toenable a user to change at least one of the two trigger phrases that areoverlapping.

In one implementation of the foregoing computer-readable medium, themethod further includes: receiving a change to one of the two triggerphrases that are overlapping via the authoring tool, wherein providingthe changed trigger phrase to the bot causes the bot to select a singletopic for conversation.

V. Conclusion

While various embodiments of the disclosed subject matter have beendescribed above, it should be understood that they have been presentedby way of example only, and not limitation. It will be understood bythose skilled in the relevant art(s) that various changes in form anddetails may be made therein without departing from the spirit and scopeof the application as defined in the appended claims. Accordingly, thebreadth and scope of the disclosed subject matter should not be limitedby any of the above-described example embodiments, but should be definedonly in accordance with the following claims and their equivalents.

What is claimed is:
 1. A system for detecting overlapping topics, thesystem comprising: a processor circuit; and a memory that stores programcode that is executed by the processor circuit to cause the system to:receive a first trigger phrase and a second trigger phrase, the firsttrigger phrase and the second trigger phrase each defining acorresponding topic of conversation for identification by a bot thatreceives the first trigger phrase or the second trigger phrase; generatea measure of similarity between the first trigger phrase and the secondtrigger phrase; detect a topic overlap based on the measure ofsimilarity being above a similarity threshold, the topic overlapindicating that the first trigger phrase and the second trigger phraseare overlapping; and provide an indication of the topic overlap to anauthoring tool.
 2. The system of claim 1, wherein the bot identifies aplurality of topics for conversation when provided with either the firsttrigger phrase or the second trigger phrase.
 3. The system of claim 1,wherein the authoring tool receives a change to one of the first triggerphrase or the second trigger phrase, and wherein the bot selects asingle topic for conversation when provided with the changed triggerphrase.
 4. The system of claim 1, wherein the measure of similarity isindicative of a degree of semantic similarity between a first vectorrepresentation corresponding to the first trigger phrase and a secondvector representation corresponding to the second trigger phrase.
 5. Thesystem of claim 1, wherein the program code that is executed by theprocessor circuit further causes the system to: provide a testinginterface that enables a simulated conversation with the bot, thesimulated conversation comprising a conversation to determine whether atrigger phrase provided to the bot causes the bot to identify aplurality of topics for conversation.
 6. The system of claim 1, whereinthe program code that is executed by the processor circuit furthercauses the system to: provide a plurality of topic overlaps to theauthoring tool, the plurality of topic overlaps ranked based on measuresof similarity corresponding to the plurality of topic overlaps.
 7. Thesystem of claim 1, wherein the program code that is executed by theprocessor circuit further causes the system to: receive, in theauthoring tool, a change to one of the first trigger phrase and thesecond trigger phrase, automatically generate an updated measure ofsimilarity based at least on the changed trigger phrase.
 8. The systemof claim 1, wherein the bot comprises a chat bot to simulate humanconversation with a user.
 9. The system of claim 1, wherein the measureof similarity is generated based on a vector representation thatcomprises an embedding generated by applying a language model to thefirst trigger phrase and the second trigger phrase.
 10. A method fordetecting overlapping topics, the method comprising: receiving a firsttrigger phrase and a second trigger phrase, the first trigger phrase andthe second trigger phrase each defining a corresponding topic ofconversation for identification by a bot that receives the first triggerphrase or the second trigger phrase; generating a measure of similaritybetween the first trigger phrase and the second trigger phrase;detecting a topic overlap based on the measure of similarity being abovea similarity threshold, the topic overlap indicating that the firsttrigger phrase and the second trigger phrase are overlapping; andproviding an indication of the topic overlap to an authoring tool. 11.The method of claim 10, wherein the bot identifies a plurality of topicsfor conversation when provided with either the first trigger phrase orthe second trigger phrase.
 12. The method of claim 10, wherein theauthoring tool receives a change to one of the first trigger phrase orthe second trigger phrase, and wherein the bot selects a single topicfor conversation when provided with the changed trigger phrase.
 13. Themethod of claim 10, wherein the measure of similarity is indicative of adegree of semantic similarity between a first vector representationcorresponding to the first trigger phrase and a second vectorrepresentation corresponding to the second trigger phrase.
 14. Themethod of claim 10, further comprising: providing a testing interfacethat enables a simulated conversation with the bot, the simulatedconversation comprising a conversation to determine whether a triggerphrase provided to the bot causes the bot to identify a plurality oftopics for conversation.
 15. The method of claim 10, further comprising:providing a plurality of topic overlaps to the authoring tool, theplurality of topic overlaps ranked based on measures of similaritycorresponding to the plurality of topic overlaps.
 16. The method ofclaim 10, further comprising: receiving, in the authoring tool, a changeto one of the first trigger phrase and the second trigger phrase,automatically generating an updated measure of similarity based at leaston the changed trigger phrase.
 17. The method of claim 10, wherein thebot comprises a chat bot to simulate human conversation with a user. 18.The method of claim 10, wherein the measure of similarity is generatedbased on a vector representation that comprises an embedding generatedby applying a language model to the first trigger phrase and the secondtrigger phrase.
 19. A computer-readable storage medium having programinstructions recorded thereon that, when executed by at least oneprocessor of a computing device, perform a method, the methodcomprising: receiving a first trigger phrase and a second triggerphrase, the first trigger phrase and the second trigger phrase eachdefining a corresponding topic of conversation for identification by abot that receives the first trigger phrase or the second trigger phrase;generating a measure of similarity between the first trigger phrase andthe second trigger phrase; detecting a topic overlap based on themeasure of similarity being above a similarity threshold, the topicoverlap indicating that the first trigger phrase and the second triggerphrase are overlapping; and providing an indication of the topic overlapto an authoring tool.
 20. The computer-readable storage medium of claim19, wherein the method further comprises: providing a plurality of topicoverlaps to the authoring tool, the plurality of topic overlaps rankedbased on measures of similarity corresponding to the plurality of topicoverlaps.